RcppGO is an optimization package for R written in C++
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Updated
Dec 14, 2021 - R
RcppGO is an optimization package for R written in C++
Simulate cognitive diagnostic model data for Deterministic Input, Noisy "And" Gate (DINA) and reduced Reparameterized Unified Model (rRUM) from Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>, Culpepper (2015) <doi:10.3102/1076998615595403>, and de la Torre (2009) <doi:10.3102/1076998607309474>.
Docker image: rocker/tidyverse + Rcpp +RcppArmadillo + remotes
Implementation of R package for the travelling salesman problem.
Como fazer seu código R ficar mais rápido com Rcpp
R and C++ code useful for time series analysis on dichotomous data
R implementation of two samples location/scale multidimensional test proposed by Barale and Shirke.
R package to sample from a Dirichlet-Dirichlet-Gamma hierarchical model
A place to keep useful Rcpp and RcppArmadillo implementations of various random number generators and math functions.
Rcpp Package implements Hierarchical Functional Models
The goal of rrum is to provide an implementation of Gibbs sampling algorithm for Bayesian Estimation of reduced Reparametrized Unifed Model (rRUM), described by Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>.
Perform a Bayesian estimation of the Exploratory reduced Reparameterized Unified Model (errum) described by Culpepper and Chen (2018) <doi:10.3102/1076998618791306>.
interface R to C, C++, Rcpp, RcppArmadillo, GSL
Answers I've given on StackOverflow
R package for residualizing covariates
R Package: Adaptively weighted group lasso for semiparametic quantile regression models
Efficient sampling of normal posterior distributions
Estimate Barton & Lord's (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x> four parameter IRT model with lower and upper asymptotes using Bayesian formulation described by Culpepper (2016) <doi:10.1007/s11336-015-9477-6>.
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